catalog_gwas_pliercanon_sep_ldclumping

Last updated: 2020-11-26

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Introduction

In this part, I considered the traits separately. I selected the SNPs with pval < 5e-8 for each traits. Then, I did LD Clumping for these SNPs to eliminate the LD and select a smaller subset of SNPs. After that, I did association tests for the plier_canonical factors with the SNPs. I also did colozalization analysis for some significant factor~snp pairs.

Material and Methods

Catalog GWAS data:

Platelet count, white blood cell count, myeloid white cell count, lymphocyte counts, red blood cell count, granulocyte count, eosinophil count, neutrophil count from Astle WJ, Elding H, Jiang T, et al. The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease. Cell. 2016;167(5):1415-1429.e19. doi:10.1016/j.cell.2016.10.042.
T2D. I first used data from our lab collaction Morris et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012 Sep;44(9):981-90. doi: 10.1038/ng.2383. Epub 2012 Aug 12. PMID: 22885922; PMCID: PMC3442244. but it doesn’t contain MAF info of variants.So I added this from GWAS Catalog: Wood AR et al. Variants in the FTO and CDKAL1 loci have recessive effects on risk of obesity and type 2 diabetes, respectively. Diabetologia. 2016 Jun;59(6):1214-21. doi: 10.1007/s00125-016-3908-5. Epub 2016 Mar 10. PMID: 26961502; PMCID: PMC4869698..
Asthma. I first used data from our lab collaction Zhu et al, Shared Genetics of Asthma and Mental Health Disorders: A Large-Scale Genome-Wide Cross-Trait Analysis. European Respiratory Journal, 2019 (PMID: 31619474) but it doesn’t contain MAF info of variants.So I added this from GWAS Catalog: Manuel A.R. et al. Genetic Architectures of Childhood- and Adult-Onset Asthma Are Partly Distinct,The American Journal of Human Genetics,Volume 104, Issue 4,2019,Pages 665-684,ISSN 0002-9297,https://doi.org/10.1016/j.ajhg.2019.02.022..
IBD,Ulcerative colitist,Crohn’s disease data are from lab collection Liu, van Sommeren et al, Nature Genetics, 2015
Waist-hip ratio data are from Shungin et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015 Feb 12;518(7538):187-196. doi: 10.1038/nature14132. PMID: 25673412; PMCID: PMC4338562.
BMI data are also from lab collection: Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, Powell C, Vedantam S, Buchkovich ML, Yang J, Croteau-Chonka DC, Esko T et al. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197-206
HDL & LDL data are downloaded from GWAS Catalog. Global Lipids Genetics Consortium., Willer, C., Schmidt, E. et al. Discovery and refinement of loci associated with lipid levels. Nat Genet 45, 12741283 (2013). https://doi.org/10.1038/ng.2797

Filtered the SNPs using pval < 5e-8 as cut off from the GWAS Catalog data.
Did LD Clumping for the SNPs in step 2. The PLINK LD Clumping patameters are:

–clump-p1 0.0001 Significance threshold for index SNPs

–clump-p2 0.01 Secondary significance threshold for clumped SNPs

–clump-r2 0.1 LD threshold for clumping

–clump-kb 1000 Physical distance threshold for clumping

For each trait, I got a subset of SNPs that are not in LD with each other.

Did association tests for plier_canonical factors and SNPs in 3. The association tests were corrected by 1)10 genotype PCs of whole genome; 2)10 PCs + GTEx:Sequencing platform,Sequencing protocol,Sex; 3)10 PCs + GTEx:Sequencing platform,Sequencing protocol,Sex + AGE
For each trait, I made a plot of association with LV(indicating by beta in GWAS) vs association with trait(indicating by ln(odds ratio) or beta in GWAS) to show if the variants have the correlated effect direction. The effect sizes of Catalog GWAS and factor association tests are harmonized by TwoSampleMR R package to make the effect alleles in these two analysis identical. The LVs have more than one significant SNPs with FDR<0.2 are included in the plotting.Besides, for each plots, I fitted the points with intercept = 0. The pvalues and r-squared are shown on the plots.
For the traits and LVs in 5, I made an info table to show more details of the SNPs.
For several LVs we are interested in, I did gene set enrichment analysis to test if the LVs are correlated with some KEGG/REACTOME pathways. I used two kind gene sets to do GSEA: 1. genes that used to compute LVs; 2. Sorting the genes in 1 by their loadings, take the top 25% as the gene set. For both gene sets, the gene scores used as input of GSEA are the gene loadings.
Resampling. For some promising trait-factor pairs, I did resampling. I resampled the SNPs without replacement, I fitted the points with intercept = 0 again and recorded the pvalues and r-squared. The resampling was repeated 1000 times. The following plots are the resampling results.

Snps after filter

After filtering by ‘pval < 5e-8’ and LD Clumping, for each trait, I got :

platelet count trait contains 688 SNPs with pval<5e-8.

white blood cell count trait contains 368 SNPs with pval<5e-8.

myeloid white cell count trait contains 319 SNPs with pval<5e-8.

lymphocyte count trait contains 436 SNPs with pval<5e-8.

red blood cell count trait contains 466 SNPs with pval<5e-8.

granulocyte count trait contains 316 SNPs with pval<5e-8.

eosinophil count trait contains 491 SNPs with pval<5e-8.

neutrophil count trait contains 317 SNPs with pval<5e-8.

IBD trait contains 116 SNPs with pval<5e-8.

Ulcerative colitist trait contains 73 SNPs with pval<5e-8.

Crohn’s disease trait contains 96 SNPs with pval<5e-8.

BMI trait contains 104 SNPs with pval<5e-8.

T2D contains 14 SNPs with pval<5e-8. T2D_2 contains 4 SNPs with pval<5e-8.

Asthma trait contains 186 SNPs with pval<5e-8. Asthma_2 trait contains 112 SNPs with pval<5e-8.

HDL trait contains 227 SNPs with pval<5e-8.

LDL trait contains 204 SNPs with pval<5e-8.

WHR trait contains 36 SNPs with pval<5e-8.

Results - pval < 5e-8 & association test covariants: 10 PCs + GTEx:Sequencing platform,Sequencing protocol,Sex + AGE

Summary table

I used ‘qvalue’ R package to compute the fdr from p-values for each SNP and made a table to show the number of SNPs that pass the threshold. The thresholds are ‘fdr < 0.1’,‘fdr < 0.2’,‘pval < 5e-8’. The ‘num_significant_pairs’ indicates the number of significant pairs under each threshold. If a trait~factor pair has as least 1 significant SNP, we named it as ‘significant pair’.

Info table

For each trait, I made a table to show the info of snps with fdr>0.2 in the factor ~ SNP + genotype pcs association test. For each trait,The LVs have more than one significant SNPs with FDR<0.2 are included.

The suffix ’_assoc’ here means that results are from factor ~ SNP + genotype pcs association test. The suffix ’_gwas’ here means results are from original GWAS results files. For EUR.CD, EUR.IBD, EUR.UC,T2D, asthma, the effectsize_gwas here means ‘ln(OR)’, for others, it means ‘beta’.

‘snp_ld’ here means the snps that in LD with the snp in each line.’ld_r2’ means the LD r-squared which is corresponding to the ‘snp_ld’ column. ‘cis-eqtl’ column indicates whether the snp is a cis-eqtl according to GTEx data. ‘cis_gene_hgnc’ and ‘cis_gene_hgnc’ is the genes that the snp influence when it act as cis-eqtl. ‘func’ and ‘func_gene’ are obtained from ANNOVAR, which indicating the snp function within the genes.

Enrichment analysis

For some promising trait-factor pairs (i.e. BMI-LV3, LV27, RBC-LV82, AsthmaLV68, WBC-LV119, Lymphocyte-LV23, LV78), I did enrichment analysis with WebGestalt. The analysis are under different settings:

ORA: For each factor, I sorted the genes that have non-zero loadings by their loadings, taking the top 25% as the gene set. The function database here are Reactome pathway; Disgenet + GLAD4U + OMIM disease dataset; geneontology biological process. The reference set affy hugene 2 0 st v1. Minimum number of genes for a category is 5, maximum number of genes for a category is 2000(default settings). All categories that have fdr<0.01 are listed.

BMI-LV27

BMI-LV76

BMI-LV90

PLT-LV49

RBC-LV82

Asthma-LV36

Asthma-LV39

WBC-LV6

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for client-side DataTables. You may consider server-side processing: https://
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WBC-LV119

Lymphocyte-LV23

Lymphocyte-LV26

LDL-LV125

WHR

Effect size plots & Resampling

For each trait, I made a plot of association with LV(indicating by beta in GWAS) vs association with trait(indicating by ln(odds ratio) or beta in GWAS) to show if the variants have the correlated effect direction. The effect sizes of Catalog GWAS and factor association tests are harmonized by TwoSampleMR R package to make the effect alleles in these two analysis identical. The LVs have more than one significant SNPs with FDR<0.2 are included in the plotting.Besides, for each plots, I fitted the points with intercept = 0. The pvalues and r-squared are shown on the plots.

I also relaxed the fdr threshold of the SNPs that used to make effect size plots(from 0.2 to 0.3/0.5)

For all pairs , I did resampling. I resampled the SNPs without replacement and fitted the points with intercept = 0 again and recorded the pvalues and r-squared. The resampling was repeated 1000 times.

I made a histogram to show the rsquared distribution from resampling. The red line in the plots are the rsquared in the origin analysis. The r_mean values in the plots are the mean values of rsquared in point fitting. The ‘p-value from resampling’ is computed by: (number of more extreme values)/(times of resampling).

Asthma_2

LV88

fdr0.2

fdr0.3

fdr0.5

LV91

fdr0.2

fdr0.3

fdr0.5

BMI

LV27

fdr0.2

fdr0.3

fdr0.5

LV30

fdr0.2

fdr0.3

fdr0.5

LV76

fdr0.2

fdr0.3

fdr0.5

LV90

fdr0.2

fdr0.3

fdr0.5

Eosinophil count

LV106

fdr0.2

fdr0.3

fdr0.5

LV125

fdr0.2

fdr0.3

fdr0.5

Crohn’s disease

LV106

fdr0.2

fdr0.3

fdr0.5

IBD

LV98

fdr0.2

fdr0.3

fdr0.5

Ulcerative colitist

LV23

fdr0.2

fdr0.3

fdr0.5

LV24

fdr0.2

fdr0.3

fdr0.5

LV108

fdr0.2

fdr0.3

fdr0.5

LDL

LV23

fdr0.2

fdr0.3

fdr0.5

LV79

fdr0.2

fdr0.3

fdr0.5

LV113

fdr0.2

fdr0.3

fdr0.5

LV117

fdr0.2

fdr0.3

fdr0.5

LV125

fdr0.2

fdr0.3

fdr0.5

granulocyte count

None of the LVs have >1 SNPs at FDR<0.2.

lymphocyte count

LV23

fdr0.2

fdr0.3

fdr0.5

LV26

fdr0.2

fdr0.3

fdr0.5

LV37

fdr0.2

fdr0.3

fdr0.5

LV65

fdr0.2

fdr0.3

fdr0.5

LV123

fdr0.2

fdr0.3

fdr0.5

myeloid white cell count

LV65

fdr0.2

fdr0.3

fdr0.5

LV94

fdr0.2

fdr0.3

fdr0.5

LV119

fdr0.2

fdr0.3

fdr0.5

neutrophil count

LV6

fdr0.2

fdr0.3

fdr0.5

LV95

fdr0.2

fdr0.3

fdr0.5

plt

LV49

fdr0.2

fdr0.3

fdr0.5

LV97

fdr0.2

fdr0.3

fdr0.5

rbc

LV3

fdr0.2

fdr0.3

fdr0.5

LV20

fdr0.2

fdr0.3

fdr0.5

LV82

fdr0.2

fdr0.3

fdr0.5

LV118

fdr0.2

fdr0.3

fdr0.5

T2D_2

Since there were only 4 SNPs left after filtering by p-values in GWAS summary data and all of them have fdr<0.2 in association test. So there is no other SNPs to do resampling.

T2D

LV1

fdr0.2

fdr0.3

fdr0.5

asthma

LV21

fdr0.2

fdr0.3

fdr0.5

LV36

fdr0.2

fdr0.3

fdr0.5

LV39

fdr0.2

fdr0.3

fdr0.5

LV68

fdr0.2

fdr0.3

fdr0.5

LV82

fdr0.2

fdr0.3

fdr0.5

wbc

LV6

fdr0.2

fdr0.3

fdr0.5

LV119

fdr0.2

fdr0.3

fdr0.5

WHR

LV1

fdr0.2

fdr0.3

fdr0.5

LV47

fdr0.2

fdr0.3

fdr0.5

LV82

fdr0.2

fdr0.3

fdr0.5

LV106

fdr0.2

fdr0.3

fdr0.5

Effect size plots - checking the reverse causality

To check if the effect size correlation is due to reverse causality: i.e. trait -> LV (trait causally affect LV), instead of LV -> trait (which is what we like to see). I used all SNPs associated with traits(pval<5E-8). The x-axis is the effects of these SNPs on trait, and y-axis is the effects on LV.

Some pair show p < 0.05, the result may be driven by the possible causal effect of LV -> trait. To test this, I removed the SNPs that are associated with LVs at FDR < 0.2 and made the plots again.

BMI

LDL

lymphocyte count

platelet count

red blood cell count

asthma

white blood cell count

WHR

QQplots

BMI

LV27, LV76 and LV90

LDL

LV125

platelet count

LV49

red blood cell count

LV82

asthma

LV36 and LV79

white blood cell count

LV6 and LV119

WHR

LV47

Colocalization – 500kb

The colocalization analysis was performed using the approximate Bayes factor test implemented in the Coloc package. Coloc computes five posterior probabilities (PP0, PP1, PP2, PP3 and PP4), each corresponding to a hypothesis: H0, no association with either trait; H1, association with trait 1 but not with trait 2; H2, association with trait 2 but not with trait 1; H3, association with trait 1 and trait 2, two independent SNPs; H4, association with trait 1 and trait 2, one shared SNP. We ran Coloc with the default parameters and used PP4 to assess evidence of colocalization. We visualized the colocalization of factor - QTLs and GWAS associations using the LocusCompareR package.

SNP selection: 1. Chose the SNPs in the info table. 2. For each SNP, the region used in colocalization analysis is between [pos-100kb, pos+100kb]. 3. All SNPs in this region are included in alalysis.

BMI

LV27

PPs in coloclization analysis
		note
nsnps	499	NA
PP.H0.abf	7.93444900060162e-146	no association with either trait
PP.H1.abf	4.61967175649592e-147	association with trait 1 but not with trait 2
PP.H2.abf	0.489837805633518	association with trait 2 but not with trait 1
PP.H3.abf	0.0280376867076828	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.482124507658802	association with trait 1 and trait 2, one shared SNP

PPs in coloclization analysis
		note
nsnps	458	NA
PP.H0.abf	3.61095771264185e-09	no association with either trait
PP.H1.abf	3.83722698846073e-10	association with trait 1 but not with trait 2
PP.H2.abf	0.301833782768097	association with trait 2 but not with trait 1
PP.H3.abf	0.0314079662287754	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.666758247008448	association with trait 1 and trait 2, one shared SNP

LV76

PPs in coloclization analysis
		note
nsnps	750	NA
PP.H0.abf	0.00544055184691358	no association with either trait
PP.H1.abf	0.000612167269311149	association with trait 1 but not with trait 2
PP.H2.abf	0.103096274108392	association with trait 2 but not with trait 1
PP.H3.abf	0.0107201932662791	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.880130813509105	association with trait 1 and trait 2, one shared SNP

PPs in coloclization analysis
		note
nsnps	551	NA
PP.H0.abf	0.00776682662078695	no association with either trait
PP.H1.abf	0.0040703879647715	association with trait 1 but not with trait 2
PP.H2.abf	0.106905431720179	association with trait 2 but not with trait 1
PP.H3.abf	0.0552002460259568	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.826057107668306	association with trait 1 and trait 2, one shared SNP

lymphocyte count

LV26

pvalues in coloclization
		note
nsnps	838916	NA
PP.H0.abf	7.30724053669563e-12	no association with either trait
PP.H1.abf	2.05169150163289e-09	association with trait 1 but not with trait 2
PP.H2.abf	0.00354391399114969	association with trait 2 but not with trait 1
PP.H3.abf	0.995041540679842	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.00141454327000869	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	676260	NA
PP.H0.abf	2.2123440534935e-09	no association with either trait
PP.H1.abf	4.64839251213925e-07	association with trait 1 but not with trait 2
PP.H2.abf	0.00472874184404426	association with trait 2 but not with trait 1
PP.H3.abf	0.993562024911887	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.00170876619247325	association with trait 1 and trait 2, one shared SNP

platelet count

LV49

pvalues in coloclization
		note
nsnps	676702	NA
PP.H0.abf	4.67388632795464e-14	no association with either trait
PP.H1.abf	6.82873385322686e-12	association with trait 1 but not with trait 2
PP.H2.abf	0.00679404873984036	association with trait 2 but not with trait 1
PP.H3.abf	0.99263697877301	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.000568972480273008	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	605098	NA
PP.H0.abf	1.2935087719358e-12	no association with either trait
PP.H1.abf	1.36709001279604e-09	association with trait 1 but not with trait 2
PP.H2.abf	0.000938470993923995	association with trait 2 but not with trait 1
PP.H3.abf	0.991848699499561	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0072128281381334	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	876044	NA
PP.H0.abf	1.74021669605641e-20	no association with either trait
PP.H1.abf	2.03875520017287e-18	association with trait 1 but not with trait 2
PP.H2.abf	0.0084579602453332	association with trait 2 but not with trait 1
PP.H3.abf	0.990893788752466	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.00064825100220002	association with trait 1 and trait 2, one shared SNP

red blood cell count

LV82

pvalues in coloclization
		note
nsnps	1146990	NA
PP.H0.abf	8.71397050347064e-13	no association with either trait
PP.H1.abf	9.95659583056139e-11	association with trait 1 but not with trait 2
PP.H2.abf	0.00866576147202876	association with trait 2 but not with trait 1
PP.H3.abf	0.990150027646962	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.00118421078057356	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	929968	NA
PP.H0.abf	9.1853160625695e-22	no association with either trait
PP.H1.abf	1.11943061922125e-19	association with trait 1 but not with trait 2
PP.H2.abf	0.00789674003843842	association with trait 2 but not with trait 1
PP.H3.abf	0.962359850028353	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0297434099332067	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	783224	NA
PP.H0.abf	7.21851454507589e-146	no association with either trait
PP.H1.abf	9.72147584626663e-144	association with trait 1 but not with trait 2
PP.H2.abf	0.00736823178852006	association with trait 2 but not with trait 1
PP.H3.abf	0.992310311354135	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.000321456857360425	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	765544	NA
PP.H0.abf	6.43711184100206e-09	no association with either trait
PP.H1.abf	7.78582393684857e-07	association with trait 1 but not with trait 2
PP.H2.abf	0.00753575703860843	association with trait 2 but not with trait 1
PP.H3.abf	0.911384792717544	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0810786652243406	association with trait 1 and trait 2, one shared SNP

white blood cell count

LV6

pvalues in coloclization
		note
nsnps	1692	NA
PP.H0.abf	2.42069422133774e-20	no association with either trait
PP.H1.abf	5.85832479206917e-21	association with trait 1 but not with trait 2
PP.H2.abf	0.752317669664185	association with trait 2 but not with trait 1
PP.H3.abf	0.18200280383898	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.0656795264968316	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	2520	NA
PP.H0.abf	0.00167804634271394	no association with either trait
PP.H1.abf	0.000518622191341393	association with trait 1 but not with trait 2
PP.H2.abf	0.190540023295325	association with trait 2 but not with trait 1
PP.H3.abf	0.0581397652226869	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.749123542947933	association with trait 1 and trait 2, one shared SNP

LV119

pvalues in coloclization
		note
nsnps	1472	NA
PP.H0.abf	1.31758242169431e-23	no association with either trait
PP.H1.abf	6.74125533437269e-24	association with trait 1 but not with trait 2
PP.H2.abf	0.639020510048892	association with trait 2 but not with trait 1
PP.H3.abf	0.326913254190781	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.034066235760328	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	512	NA
PP.H0.abf	8.45984458247953e-94	no association with either trait
PP.H1.abf	4.53826813191688e-95	association with trait 1 but not with trait 2
PP.H2.abf	0.793985073521508	association with trait 2 but not with trait 1
PP.H3.abf	0.0424295985213398	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.163585327957141	association with trait 1 and trait 2, one shared SNP

white blood cell count

LV47

pvalues in coloclization
		note
nsnps	433	NA
PP.H0.abf	3.63129877087665e-07	no association with either trait
PP.H1.abf	2.19315268516939e-08	association with trait 1 but not with trait 2
PP.H2.abf	0.282771374037345	association with trait 2 but not with trait 1
PP.H3.abf	0.0163773582482725	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.700850882652979	association with trait 1 and trait 2, one shared SNP

pvalues in coloclization
		note
nsnps	807	NA
PP.H0.abf	4.0905285744147e-08	no association with either trait
PP.H1.abf	4.27896985899567e-09	association with trait 1 but not with trait 2
PP.H2.abf	0.198547668391827	association with trait 2 but not with trait 1
PP.H3.abf	0.019987966162058	association with trait 1 and trait 2,two independent SNPs
PP.H4.abf	0.781464320261861	association with trait 1 and trait 2, one shared SNP

Results - pval < 5e-8 & association test covariants: 10 PCs

Summary table

Info table

The suffix ’_assoc’ here means that results are from factor ~ SNP + genotype pcs association test. The suffix ’_gwas’ here means results are from original GWAS results files. For EUR.CD, EUR.IBD, EUR.UC, the effectsize_gwas here means ‘ln(OR)’, for others, it means ‘beta’.

Enrichment analysis

ORA: For each factor, I sorted the genes in 1 by their loadings, taking the top 25% as the gene set. The function database here are Reactome pathway; Disgenet + GLAD4U + OMIM disease dataset; geneontology biological process. The reference set affy hugene 2 0 st v1. Minimum number of genes for a category is 5, maximum number of genes for a category is 2000(default settings). All categories that have fdr<0.01 are listed.

GSEA: For each factor, I used all genes (zero loading genes included), taking the gene loadings as their gene score in GSEA. The function database is Reactome pathway.

BMI-LV3-Reactome BMI-LV3-GO BMI-LV3-phenotype BMI-LV3-disease-Disgenet

BMI-LV27-Reactome BMI-LV27-GO BMI-LV27-phenotype BMI-LV27-disease-Disgenet

BMI-LV76-Reactome BMI-LV76-GO BMI-LV76-phenotype BMI-LV76-disease-Disgenet

RBC-LV82-Reactome RBC-LV82-GO RBC-LV82-phenotype RBC-LV82-disease-Disgenet

Asthma-LV68-Reactome Asthma-LV68-GO Asthma-LV68-phenotype Asthma-LV68-disease-Disgenet

WBC-LV119-Reactome WBC-LV119-GO WBC-LV119-phenotype WBC-LV119-disease-Disgenet

Lymphocyte-LV23-Reactome Lymphocyte-LV23-GO Lymphocyte-LV23-phenotype Lymphocyte-LV23-disease-Disgenet

Lymphocyte-LV78 Lymphocyte-LV78-GO Lymphocyte-LV78-phenotype Lymphocyte-LV78-disease-Disgenet

GSEA: For each factor, I used all genes (zero loading genes included), taking the gene loadings as their gene score in GSEA. For those loadings = 0, I assigned a random number from normal distribution N(0,1e-5) to it to avoid ties. The function database is Reactome pathway.

Effect size plots

BMI

Eosinophil count

Crohn’s disease

IBD

Ulcerative colitist

granulocyte count

lymphocyte count

myeloid white cell count

neutrophil count

None of the LVs have >1 SNPs at FDR<0.2.

plt

None of the LVs have >1 SNPs at FDR<0.2.

rbc

asthma

wbc

effect size plots – more SNPs

For some promising trait-factor pairs (i.e. BMI-LV3, LV27,LV76, RBC-LV82, Asthma-LV68, WBC-LV119, Lymphocyte-LV23, LV78), I relaxed the fdr threshold of the SNPs that used to make effect size plots(from 0.2 to 0.3/0.5)

I also made a plot to show the the distribution of the SNPs’ fdr.

BMI

Crohn’s disease

The CD~lv88 is not very promising pairs when considering SNPs at fdr<0.2, but the fitting result at fdr <0.5 is better than the former result . So I post the plots here too.

lymphocyte count

red blood cell count

The rbc~lv42 and rbc~lv59 pairs are not very promising pairs when considering SNPs at fdr<0.2, but the fitting results at fdr <0.5 are better than the former results. So I post the plots here too.

asthma

white blood cell count

Effect size plots - checking the reverse causality

Some pair show p < 0.05, the result may be driven by the possible causal effect of LV -> trait. To test this, I removed the SNPs that are associated with LVs at FDR < 0.2 and made the plots again.

BMI

Crohn’s disease

The CD~lv88 is not very promising pairs when considering SNPs at fdr<0.2, but the fitting result at fdr <0.5 is better than the former result . So I post the plots here too.

lymphocyte count

red blood cell count

The rbc~lv42 and rbc~lv59 pairs are not very promising pairs when considering SNPs at fdr<0.2, but the fitting results at fdr <0.5 are better than the former results. So I post the plots here too.

asthma

white blood cell count

Results - pval < 5e-8 & association test covariants: 10 PCs + GTEx:Sequencing platform,Sequencing protocol,Sex

Summary table

Info table

The suffix ’_assoc’ here means that results are from factor ~ SNP + genotype pcs association test. The suffix ’_gwas’ here means results are from original GWAS results files. For EUR.CD, EUR.IBD, EUR.UC, the effectsize_gwas here means ‘ln(OR)’, for others, it means ‘beta’.

Effect size plots

BMI

Eosinophil count

Crohn’s disease

IBD

Ulcerative colitist

granulocyte count

None of the LVs have >1 SNPs at FDR<0.2.

lymphocyte count

myeloid white cell count

neutrophil count

plt

rbc

T2D

None of the LVs have >1 SNPs at FDR<0.2.

asthma

wbc

Effect size plots- more SNPs

For some promising trait-factor pairs , I relaxed the fdr threshold of the SNPs that used to make effect size plots(from 0.2 to 0.3/0.5)

BMI

The BMI~lv90 is not very promising pairs when considering SNPs at fdr<0.2, but the fitting result at fdr <0.5 is better than the former result . So I post the plots here too.

lymphocyte count

red blood cell count

asthma

white blood cell count

Effect size plots - checking the reverse causality

Some pair show p < 0.05, the result may be driven by the possible causal effect of LV -> trait. To test this, I removed the SNPs that are associated with LVs at FDR < 0.2 and made the plots again.

BMI

lymphocyte count

red blood cell count

asthma

white blood cell count

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5        rstudioapi_0.11   whisker_0.3-2     knitr_1.30       
 [5] magrittr_1.5      R6_2.4.1          rlang_0.4.8       highr_0.8        
 [9] stringr_1.4.0     tools_3.6.1       DT_0.15           xfun_0.18        
[13] git2r_0.26.1      crosstalk_1.1.0.1 htmltools_0.5.0   ellipsis_0.3.1   
[17] rprojroot_1.3-2   yaml_2.2.1        digest_0.6.25     tibble_3.0.3     
[21] lifecycle_0.2.0   crayon_1.3.4      later_1.1.0.1     htmlwidgets_1.5.2
[25] vctrs_0.3.4       promises_1.1.1    fs_1.5.0          glue_1.4.2       
[29] evaluate_0.14     rmarkdown_1.13    stringi_1.5.3     compiler_3.6.1   
[33] pillar_1.4.6      backports_1.1.10  jsonlite_1.7.1    httpuv_1.5.1     
[37] pkgconfig_2.0.3

absolute	relative
/project2/xinhe/xsun/website/factor_analysis/output/sum_sep_tog_pval_pliercanon_wgpc_ldcl_d1k_ageco.rdata	output/sum_sep_tog_pval_pliercanon_wgpc_ldcl_d1k_ageco.rdata
/project2/xinhe/xsun/website/factor_analysis/output/info_pval5e8_pliercanon_ld_d1k_ageco.rdata	output/info_pval5e8_pliercanon_ld_d1k_ageco.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv27top025_pliercanon.rdata	output/lv27top025_pliercanon.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv76top025_pliercanon.rdata	output/lv76top025_pliercanon.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv90top025_pliercanon.rdata	output/lv90top025_pliercanon.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv49top025_pliercanon.rdata	output/lv49top025_pliercanon.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv82top025_pliercanon.rdata	output/lv82top025_pliercanon.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv36top025_pliercanon.rdata	output/lv36top025_pliercanon.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv39top025_pliercanon.rdata	output/lv39top025_pliercanon.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv6top025_pliercanon.rdata	output/lv6top025_pliercanon.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv119top025_pliercanon.rdata	output/lv119top025_pliercanon.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv23top025_pliercanon.rdata	output/lv23top025_pliercanon.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv26top025_pliercanon.rdata	output/lv26top025_pliercanon.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv125top025_pliercanon.rdata	output/lv125top025_pliercanon.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lv47top025_pliercanon.rdata	output/lv47top025_pliercanon.rdata
/project2/xinhe/xsun/website/factor_analysis/output/bmi_27_53759010.coloc_pliercanon_d1k_500.rdata	output/bmi_27_53759010.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/bmi_27_85758440.coloc_pliercanon_d1k_500.rdata	output/bmi_27_85758440.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/bmi_76_40380914.coloc_pliercanon_d1k_500.rdata	output/bmi_76_40380914.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/bmi_76_43842728.coloc_pliercanon_d1k_500.rdata	output/bmi_76_43842728.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/LDL_125_160687504.coloc_pliercanon_d1k_500.rdata	output/LDL_125_160687504.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/LDL_125_44943964.coloc_pliercanon_d1k_500.rdata	output/LDL_125_44943964.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/LDL_125_56955678.coloc_pliercanon_d1k_500.rdata	output/LDL_125_56955678.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/LDL_125_56965346.coloc_pliercanon_d1k_500.rdata	output/LDL_125_56965346.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lymph_26_41686157.coloc_pliercanon_d1k_500.rdata	output/lymph_26_41686157.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/lymph_26_59258463.coloc_pliercanon_d1k_500.rdata	output/lymph_26_59258463.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/plt_49_34414160.coloc_pliercanon_d1k_500.rdata	output/plt_49_34414160.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/plt_49_56786080.coloc_pliercanon_d1k_500.rdata	output/plt_49_56786080.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/plt_49_88978375.coloc_pliercanon_d1k_500.rdata	output/plt_49_88978375.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/rbc_82_32506416.coloc_pliercanon_d1k_500.rdata	output/rbc_82_32506416.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/rbc_82_42718545.coloc_pliercanon_d1k_500.rdata	output/rbc_82_42718545.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/rbc_82_54482753.coloc_pliercanon_d1k_500.rdata	output/rbc_82_54482753.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/rbc_82_8614076.coloc_pliercanon_d1k_500.rdata	output/rbc_82_8614076.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/wbc_6_129655816.coloc_pliercanon_d1k_500.rdata	output/wbc_6_129655816.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/wbc_6_81575948.coloc_pliercanon_d1k_500.rdata	output/wbc_6_81575948.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/wbc_119_16589041.coloc_pliercanon_d1k_500.rdata	output/wbc_119_16589041.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/wbc_119_31355703.coloc_pliercanon_d1k_500.rdata	output/wbc_119_31355703.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/WHR_47_53955989.coloc_pliercanon_d1k_500.rdata	output/WHR_47_53955989.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/WHR_47_6742916.coloc_pliercanon_d1k_500.rdata	output/WHR_47_6742916.coloc_pliercanon_d1k_500.rdata
/project2/xinhe/xsun/website/factor_analysis/output/sum_sep_tog_pval_pliercanon_wgpc_ldcl_d1k.rdata	output/sum_sep_tog_pval_pliercanon_wgpc_ldcl_d1k.rdata
/project2/xinhe/xsun/website/factor_analysis/output/info_pval5e8_pliercanon_ld_d1k.rdata	output/info_pval5e8_pliercanon_ld_d1k.rdata
/project2/xinhe/xsun/website/factor_analysis/output/sum_sep_tog_pval_pliercanon_wgpc_ldcl_d1k_noageco.rdata	output/sum_sep_tog_pval_pliercanon_wgpc_ldcl_d1k_noageco.rdata
/project2/xinhe/xsun/website/factor_analysis/output/info_pval5e8_pliercanon_ld_d1k_noageco.rdata	output/info_pval5e8_pliercanon_ld_d1k_noageco.rdata

catalog_gwas_pliercanon_sep_ldclumping_r01d1000

XSun

2020-10-14

Introduction

Material and Methods

Snps after filter

Results - pval < 5e-8 & association test covariants: 10 PCs + GTEx:Sequencing platform,Sequencing protocol,Sex + AGE

Summary table

Info table

Enrichment analysis

Effect size plots & Resampling

Asthma_2

BMI

Eosinophil count

Crohn’s disease

IBD

Ulcerative colitist

LDL

granulocyte count

lymphocyte count

myeloid white cell count

neutrophil count

plt

rbc

T2D_2

T2D

asthma

wbc

WHR

Effect size plots - checking the reverse causality

BMI

LDL

lymphocyte count

platelet count

red blood cell count

asthma

white blood cell count

WHR

QQplots

BMI

LDL

platelet count

red blood cell count

asthma

white blood cell count

WHR

Colocalization – 500kb

BMI

lymphocyte count

platelet count

red blood cell count

white blood cell count

white blood cell count

Results - pval < 5e-8 & association test covariants: 10 PCs

Summary table

Info table

Enrichment analysis

Effect size plots

BMI

Eosinophil count

Crohn’s disease

IBD

Ulcerative colitist

granulocyte count

lymphocyte count

myeloid white cell count

neutrophil count

plt

rbc

asthma

wbc

effect size plots – more SNPs

BMI

Crohn’s disease

lymphocyte count

red blood cell count

asthma

white blood cell count

Effect size plots - checking the reverse causality

BMI